Classification of Microcalcification Clusters via PSOKNN Heuristic Parameter Selection and GLCM Features
نویسندگان
چکیده
منابع مشابه
Classification of Microcalcification Clusters via PSO-KNN Heuristic Parameter Selection and GLCM Features
Texture-based computer-aided diagnosis (CADx) of microcalcification clusters is more robust than the state-of-art shape-based CADx because the performance of shape-based approach heavily depends on the effectiveness of microcalcification (MC) segmentation. This paper presents a texture-based CADx that consists of two stages. The first one characterizes MC clusters using texture features from gr...
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The presence of microcalcification clusters is a primary sign of breast cancer. It is difficult and time consuming for radiologists to diagnose microcalcifications. In this paper, we present a novel method for the classification of malignant and benign microcalcification clusters in mammograms. We analyse the topology of individual microcalcifications within a cluster using multiscale morpholog...
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Characterizing the texture of mammographic tissue is an efficient and robust tool for the diagnosis of microcalcification (MC) clusters in mammography because it does not require a prior MC segmentation stage. This work is not only intended to validate MCs’ surrounding tissue hypothesis that reveals the potential of breast tissue surrounding MCs to diagnose microcalcifications, but to present a...
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We developed an ensemble classifier for the task of computer-aided diagnosis of breast microcalcification clusters, which are very challenging to characterize for radiologists and computer models alike. The purpose of this study is to help radiologists identify whether suspicious calcification clusters are benign vs. malignant, such that they may potentially recommend fewer unnecessary biopsies...
متن کاملTopological Modelling and Classification of Mammographic Microcalcification Clusters
Goal: The presence of microcalcification clusters is a primary sign of breast cancer; however, it is difficult and time consuming for radiologists to classify microcalcifications as malignant or benign. In this paper, a novel method for the classification of microcalcification clusters in mammograms is proposed. Methods: The topology/connectivity of individual microcalcifications is analysed wi...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2011
ISSN: 0975-8887
DOI: 10.5120/3798-5235